ATPC2 Antibody

What is ATP2C2 and what is its significance in cancer research?

ATP2C2 (ATPase Secretory Pathway Ca2+ Transporting 2) is a gene that encodes a calcium-transporting ATPase protein involved in calcium homeostasis. Recent studies have identified ATP2C2 as a novel immune-related marker with significant implications in tumor microenvironment (TME) regulation. Research indicates that ATP2C2 is overexpressed in several malignancies compared to normal tissues, including breast cancer and specifically triple-negative breast cancer (TNBC) . Its expression is associated with patient prognosis, making it an important target for cancer research. ATP2C2 has been linked to immunological responses and may serve as a biomarker for diagnosing and treating cancer, particularly TNBC .

How does ATP2C2 expression correlate with clinical outcomes in cancer patients?

Analysis of clinical data demonstrates that ATP2C2 expression is negatively correlated with patient survival in breast cancer. Specifically, patients with low ATP2C2 expression have a significantly higher survival rate compared to those with high expression . Studies using the GEPIA database confirm that breast cancer patients with low ATP2C2 expression had considerably greater survival rates than those with high expression . Additionally, ATP2C2 expression has been found to correlate with clinicopathological features including age and tumor staging (T- and N-staging) . This prognostic value makes ATP2C2 a potential biomarker for predicting patient outcomes and informing treatment decisions.

What is the relationship between ATP2C2 and tumor microenvironment (TME)?

ATP2C2 expression appears to significantly influence the tumor microenvironment in cancer. Research using ESTIMATE and CIBERSORT algorithms has revealed that ATP2C2 expression levels correlate with the abundance and distribution of tumor-infiltrating immune cells (TICs) . Gene Set Enrichment Analysis (GSEA) demonstrates that low ATP2C2 expression is associated with immune-related activities, while high ATP2C2 expression correlates with metabolic pathways . This suggests that ATP2C2 may serve as an indicator of TME status, potentially influencing the balance between immune-dominant and immune-suppressive microenvironments in tumors.

What methodologies are most effective for studying ATP2C2 expression in tumor samples?

Several complementary methodologies have proven effective for comprehensive ATP2C2 analysis:

Transcriptomic Analysis: RNA sequencing or microarray data can be analyzed using public databases like TCGA and GEO to assess ATP2C2 mRNA expression levels across different cancer types and normal tissues .

Immunohistochemistry (IHC): For protein-level detection, IHC using rabbit polyclonal ATP2C2 antibody (1:200, NBP2-14329, NOVUS) has been successfully employed. The protocol typically involves incubating 3-μm-thick paraffin-embedded sections with the antibody at 4°C overnight, followed by peroxidase-conjugated anti-rabbit IgG (1:1,000) incubation at 37°C for 30 minutes and BAD staining .

Computational Methods: Several algorithms provide valuable insights into ATP2C2's role:

• ESTIMATE algorithm: Evaluates tumor purity and identifies TME-related biomarkers

• CIBERSORT: Determines the proportions of 22 immune cell types in the TME

• GSEA: Identifies pathways enriched in different ATP2C2 expression subgroups

• TIDE algorithm: Analyzes relationships between ATP2C2, cytotoxic T lymphocytes, and immune checkpoint inhibitor efficacy

How does ATP2C2 influence specific immune cell populations in the tumor microenvironment?

ATP2C2 expression shows significant associations with multiple immune cell populations. Research using the xCell algorithm demonstrated that 16 tumor-infiltrating immune cells differed significantly between high and low ATP2C2 expression groups . Specifically:

• In low ATP2C2 expression groups: Higher proportions of naïve B cells, γδ T cells, activated memory CD4+ T cells, naïve CD4+ T cells, monocytes, M0 macrophages, M2 macrophages, and activated dendritic cells were observed .

• In high ATP2C2 expression groups: Greater proportions of regulatory T cells (Tregs), activated NK cells, and M1 macrophages were found .

Pearson's correlation analysis further revealed that ATP2C2 expression positively correlates with T follicular helper (Tfh) cells and negatively correlates with gamma delta (γδ) T cells . This suggests ATP2C2 may play a role in maintaining specific immune environments within tumors, potentially influencing anti-tumor immunity.

What molecular pathways and functions are associated with ATP2C2 expression?

Gene Set Enrichment Analysis (GSEA) has revealed distinct pathway enrichment patterns associated with ATP2C2 expression levels:

Low ATP2C2 expression groups: Genes are predominantly enriched in immune-related activities, including:

• Adaptive immune response

• Lymphocyte activation and differentiation

• Positive regulation of immune response

• Regulation of immune system processes

• T cell activation

• Cytokine-cytokine receptor interaction

• Hematopoietic cell lineage

• Primary immunodeficiency

High ATP2C2 expression groups: Genes are primarily enriched in:

• Keratinization-related pathways (cornification, keratin filament, skin development)

• Metabolic pathways (citrate cycle/TCA cycle)

• Amino sugar and nucleotide sugar metabolism

• Fructose and mannose metabolism

These divergent pathway enrichments suggest ATP2C2 may function as a molecular switch between immune-dominant and metabolic-dominant states within the tumor microenvironment.

How might ATP2C2 expression influence the efficacy of immune checkpoint inhibitors (ICIs)?

Analysis using the TIDE (Tumor Immune Dysfunction and Exclusion) algorithm has revealed important relationships between ATP2C2 expression and potential response to immunotherapy. In breast cancer patients with low ATP2C2 expression, high cytotoxic T lymphocyte (CTL) levels correlate with better prognosis, while this benefit is not observed in patients with high ATP2C2 expression .

The TIDE algorithm further predicts that ATP2C2 expression levels may relate to enhanced efficacy of ICI treatment in certain cancer types, including melanoma and kidney renal clear cell carcinoma (KIRC) . These findings suggest ATP2C2 may influence immune evasion mechanisms and responsiveness to immunotherapy, potentially serving as a predictive biomarker for ICI efficacy.

What controls should be included when using ATP2C2 antibodies for immunohistochemistry?

When conducting immunohistochemistry with ATP2C2 antibodies, several controls are essential:

Positive Controls: Include tissue samples known to express ATP2C2, such as TNBC samples with confirmed high expression. Studies have validated that ATP2C2 protein expression is higher in TNBC tissues compared to paired normal tissues .

Negative Controls: Include adjacent normal breast tissue as negative controls, as ATP2C2 expression is typically lower in normal tissue compared to tumor samples . Additionally, perform technical negative controls by omitting the primary antibody while maintaining all other steps in the protocol.

Antibody Validation: Verify antibody specificity through western blotting or other complementary techniques before proceeding with IHC. The rabbit polyclonal ATP2C2 antibody (NBP2-14329, NOVUS) at 1:200 dilution has been successfully used in published research .

Protocol Standardization: Standardize tissue processing, antigen retrieval (Tris/EGTA buffer, pH=9), and detection methods (such as peroxidase-conjugated anti-rabbit IgG at 1:1,000 dilution) .

How can researchers develop TMErisk models incorporating ATP2C2 expression data?

Developing a TMErisk model that includes ATP2C2 requires a systematic approach:

• Data Collection and Preprocessing: Obtain gene expression data from repositories like TCGA or GEO. Normalize data and remove batch effects if combining multiple datasets.

• Identification of TME-Related Genes: Use the ESTIMATE algorithm to calculate ImmuneScore, StromalScore, and ESTIMATEScore for each sample. Divide samples into high and low score groups to identify differentially expressed genes related to TME .

• Selection of Prognostic Genes: Apply univariate Cox regression analysis to identify TME-related genes significantly associated with survival. Research has identified ATP2C2 among key genes strongly correlated with prognosis .

• Model Construction: Employ multivariate Cox regression analysis to develop a risk score formula. For example:
  Risk score = Σ(Coefficient of Gene × Expression of Gene)

• Model Validation: Divide samples into training and validation cohorts. Evaluate model performance using Kaplan-Meier survival analysis and time-dependent ROC curves to assess predictive accuracy for outcomes such as 3-year and 5-year survival rates .

• Assessment of Independent Prognostic Value: Perform multivariate Cox analysis with clinical variables to determine if the model provides independent prognostic information beyond traditional clinical factors.

What are the current limitations in ATP2C2 research and how might they be addressed?

Several limitations exist in current ATP2C2 research:

Sample Size Constraints: Many studies have limited sample sizes, particularly for protein-level validation. For example, one study used only 20 TNBC patient samples for immunohistochemical validation . Future research should include larger, more diverse patient cohorts.

Follow-up Duration: Short follow-up periods limit assessment of long-term prognostic value. As noted in one study: "because of the limited number of available TNBC samples and the short duration of present of the disease in the included patients, it was impossible to report the condition in a 10-year period" . Longer follow-up studies are needed.

Functional Mechanisms: The precise molecular mechanisms by which ATP2C2 influences tumor microenvironment and immune cell populations remain incompletely understood. Further in vitro and in vivo functional studies are needed to elucidate these mechanisms.

Cancer Type Specificity: Most research has focused on breast cancer, particularly TNBC. Studies across multiple cancer types would provide a more comprehensive understanding of ATP2C2's role in cancer biology.

Therapeutic Implications: Limited data exists on how ATP2C2 manipulation might affect treatment responses. Investigations into ATP2C2 as a therapeutic target or as a biomarker for specific therapies should be expanded.

How can researchers integrate ATP2C2 data with other biomarkers for comprehensive cancer profiling?

Integration of ATP2C2 with other biomarkers can enhance cancer profiling through several approaches:

Multi-omics Integration: Combine ATP2C2 expression data with:

• Genomic data (mutations, CNVs)

• Epigenomic profiles (DNA methylation, histone modifications)

• Proteomic and phosphoproteomic data

• Metabolomic signatures

TME Component Analysis: Integrate ATP2C2 expression with comprehensive TME characterization including:

• Spatial distribution of immune cells (using techniques like spatial transcriptomics)

• Immune checkpoint expression profiles

• Cytokine/chemokine networks

Machine Learning Approaches: Develop machine learning models that incorporate ATP2C2 alongside other biomarkers to improve prediction of:

• Patient prognosis

• Treatment response

• Immune infiltration patterns

Clinical Data Integration: Combine ATP2C2 expression with clinical variables such as age, tumor stage, and treatment history to create more robust prognostic and predictive models.

Research has already demonstrated the feasibility of developing TMErisk models that incorporate ATP2C2 alongside other immune-related genes like P3H2 and SCN3B , indicating the potential for expanded integrated approaches.

What is the relationship between ATP2C2 and immunotherapy resistance mechanisms?

The TIDE algorithm analysis reveals that ATP2C2 expression may influence immunotherapy response through several mechanisms:

In patients with low ATP2C2 expression, high cytotoxic T lymphocyte (CTL) levels correlate with better prognosis, while this relationship is not observed in patients with high ATP2C2 expression . This suggests ATP2C2 may contribute to T cell dysfunction or exclusion mechanisms.

The relationship between ATP2C2 and immune cell populations provides further insights into potential resistance mechanisms. High ATP2C2 expression correlates with increased regulatory T cells (Tregs) and M1 macrophages, but decreased activated dendritic cells, activated memory CD4+ T cells, and γδ T cells . These shifts in immune cell populations may create an immunosuppressive microenvironment that limits immunotherapy efficacy.

GSEA results further support this hypothesis, showing that low ATP2C2 expression correlates with immune-related pathways, while high expression correlates with metabolic and keratinization pathways . This pathway shift may represent a mechanism by which tumors with high ATP2C2 expression evade immune surveillance.

How might single-cell analysis enhance our understanding of ATP2C2's role in the tumor microenvironment?

Single-cell technologies offer tremendous potential to advance ATP2C2 research:

Cellular Heterogeneity: Single-cell RNA sequencing (scRNA-seq) could reveal ATP2C2 expression patterns across diverse cell populations within tumors, identifying specific cell types that express ATP2C2 and how this influences their function.

Spatial Context: Spatial transcriptomics or multiplexed immunofluorescence could map ATP2C2 expression in relation to specific immune cell populations within the tumor microenvironment, providing insights into spatial relationships that bulk analysis cannot capture.

Dynamic Processes: Single-cell trajectory analysis could elucidate how ATP2C2 expression changes during processes such as T cell exhaustion, macrophage polarization, or cancer progression.

Cell-Cell Interactions: Single-cell analysis could help identify potential ligand-receptor interactions between ATP2C2-expressing cells and other cells in the TME, revealing intercellular communication networks.

Rare Cell Populations: Single-cell approaches could detect ATP2C2's influence on rare but functionally important cell populations that might be missed in bulk analysis, such as specific T cell subsets or cancer stem cells.